Authors - Lanja Azeez Abdalqadir, Aram Mahmood Ahmed, Rozha Kamal Ahmed, Dirk Draheim Abstract - This study explores advanced metaheuristic optimization algorithms to improve smart home energy management under constrained electricity supply, aiming to reduce costs and enhance energy efficiency. It addresses challenges such as dynamic pricing and unstable supply, particularly common in developing regions. Five algorithms—Particle Swarm Optimization (PSO), Bat Algorithm (BAT), Fitness Dependent Optimization (FDO), Marine Predators Algorithm (MPA), and Single Candidate Optimization (SCO)—are evaluated, along with enhanced versions of MPA, FDO, and SCO incorporating Lévy flight and Oppo-sition-Based Learning (OBL). OBL improves exploration and exploitation in FDO and MPA, while Lévy flight enhances SCO’s ability to escape local optima. A novel cyclic rebounding technique is introduced to manage appliance sched-ules exceeding 24-hour limits. Tested across three scheduling scenarios, results show that MPA-OBL consistently achieves the lowest energy costs. Overall, the proposed enhancements significantly improve energy optimization in supply-constrained environments.
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